def test_prevalence_comparison(self):
# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)
# Model selection
model = sir.SIRModel(g)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.001)
cfg.add_model_parameter('gamma', 0.02)
cfg.add_model_parameter("percentage_infected", 0.01)
model.set_initial_status(cfg)
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
model1 = si.SIModel(g)
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.001)
cfg.add_model_parameter("percentage_infected", 0.01)
model1.set_initial_status(cfg)
iterations = model1.iteration_bunch(200)
trends1 = model1.build_trends(iterations)
viz = DiffusionPrevalenceComparison([model, model1], [trends, trends1])
viz.plot("prev_comparison.pdf")
os.remove("prev_comparison.pdf")
def test_si_model(self):
g = nx.erdos_renyi_graph(1000, 0.1)
model = si.SIModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.5)
config.add_model_parameter("percentage_infected", 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
def si_model(g, er_g, ba_g):
print '\nSI MODEL SIMULATION'
print 'MODEL CONFIGURATION'
cfg = mc.Configuration()
beta = float(raw_input('INFECTION RATE: '))
cfg.add_model_parameter('beta', beta )
cfg.add_model_parameter('percentage_infected',
float(raw_input('PERCENTAGE INFECTED: ')))
for_comparison = dict()
for network in [g, er_g, ba_g]:
model_si = si.SIModel(network)
model_si.set_initial_status(cfg)
iterations = model_si.iteration_bunch(200)
trends_si = model_si.build_trends(iterations)
if network is g:
print 'Original graph'
model_si.name = 'G'
viz = DiffusionTrend(model_si, trends_si)
viz.plot('../../report/images/spreading/si/diffusion.pdf')
viz = DiffusionPrevalence(model_si, trends_si)
viz.plot('../../report/images/spreading/si/prevalence.pdf')
for_comparison['original_si'] = [model_si, trends_si]
elif network is er_g:
print 'ER graph'
model_si.name = 'ER'
viz = DiffusionTrend(model_si, trends_si)
viz.plot('../../report/images/spreading/si/diffusion_er.pdf')
viz = DiffusionPrevalence(model_si, trends_si)
viz.plot('../../report/images/spreading/si/prevalence_er.pdf')
for_comparison['er_si'] = [model_si, trends_si]
else:
print 'BA graph'
model_si.name = 'BA'
viz = DiffusionTrend(model_si, trends_si)
viz.plot('../../report/images/spreading/si/diffusion_ba.pdf')
viz = DiffusionPrevalence(model_si, trends_si)
viz.plot('../../report/images/spreading/si/prevalence_ba.pdf')
for_comparison['ba_si'] = [model_si, trends_si]
viz = DiffusionTrendComparison([
for_comparison['original_si'][0],
for_comparison['er_si'][0],
for_comparison['ba_si'][0]
],
[
for_comparison['original_si'][1],
for_comparison['er_si'][1],
for_comparison['ba_si'][1]
])
viz.plot("../../report/images/spreading/si/trend_comparison_beta{}.pdf".format(beta))
def get_epidemic_si(graph, beta, perc_inf, infected_nodes):
model_si = si.SIModel(graph)
cfg_si = mc.Configuration()
cfg_si.add_model_parameter("beta", beta)
cfg_si.add_model_parameter("percentage_infected", perc_inf)
cfg_si.add_model_parameter("Infected", infected_nodes)
model_si.set_initial_status(cfg_si)
iteration_si = model_si.iteration_bunch(50)
trends_si = model_si.build_trends(iteration_si)
viz_si = DiffusionTrend(model_si, trends_si)
viz_si.plot()
def test_multiple_si_model(self):
g = nx.erdos_renyi_graph(1000, 0.1)
model = si.SIModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.01)
config.add_model_parameter("percentage_infected", 0.1)
model.set_initial_status(config)
executions = ut.multi_runs(model,
execution_number=10,
iteration_number=50)
self.assertEqual(len(executions), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
def simulate_si_on_barabasi_albert():
print('######################################')
print('Simulating SI Model on Barabasi-Albert Graph...')
print('######################################')
g = nx.barabasi_albert_graph(1000, 10)
model = si.SIModel(g)
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.01)
cfg.add_model_parameter("percentage_infected", 0.05)
model.set_initial_status(cfg)
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
draw_epidemic_plot(model, trends)
def simulate_si_on_erdos_renyi():
print('######################################')
print('Simulating SI Model on Erdős-Rényi Graph...')
print('######################################')
g = nx.erdos_renyi_graph(1000, 0.15)
model = si.SIModel(g)
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.01)
cfg.add_model_parameter("percentage_infected", 0.05)
model.set_initial_status(cfg)
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
draw_epidemic_plot(model, trends)
def siModel(filename):
# Network topology
g = nx.read_edgelist(filename, nodetype=int, data=(('weight',float),), create_using=nx.Graph())
# Model selection
model = si.SIModel(g)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.34)
cfg.add_model_parameter("percentage_infected", 0)
model.set_initial_status(cfg)
col=[]
sus=[]
inf=[]
# Simulation execution
iterations = model.iteration_bunch(4)
for i in iterations:
iter_ind=i['iteration']
#c=i['node_count']
node_status_list=i['status']
#print("Iteration:",iter_ind)
#print(node_status_list)
listOfKeys = [key for (key, value) in node_status_list.items() if value == 1]
#print(listOfKeys)
if(listOfKeys != []):
inf.extend(listOfKeys)
#print("infected nodes = ", inf)
print("total nodes", len(g))
print("infected nodes", len(inf))
if(len(inf) > 5000):
sys.exit()
h = g.subgraph(inf)
#print(len(h))
edgesOfInfectedGraph = list(h.edges())
f = open('DiffusionModel/InfectedGraph.txt', 'w')
f.write(str(inf[0]) + '\n')
for t in edgesOfInfectedGraph:
line = ' '.join(str(x) for x in t)
#print(line)
f.write(line + '\n')
f.close()
#nx.draw_spring(h, cmap = plt.get_cmap('jet'), node_size=100, with_labels= True)
#plt.show()
return inf[0], h
def siModel(graph, iteration):
# Model Selection
si_model = si.SIModel(graph)
# Model Configuration
config = mc.Configuration()
config.add_model_parameter('beta', 0.005)
config.add_model_parameter("percentage_infected", 0.001)
si_model.set_initial_status(config)
# Simulation
iterations = si_model.iteration_bunch(iteration)
trends = si_model.build_trends(iterations)
viz = DiffusionTrend(si_model, trends)
p = viz.plot(width=800, height=800)
show(p)
# Network topology
#H = nx.karate_club_graph()
fh=open("../../DataSet/blogs.txt", 'rb')
H = nx.read_weighted_edgelist(fh,nodetype=int, encoding='utf-8')
fh.close()
#H = nx.convert_node_labels_to_integers(G, first_label=0, ordering="default", label_attribute = None)
b=nx.density(H)#*3
#b=1
ex=5
it=30
#seed nodes
#--------------------------------------------------------------
# Model selection
model = si.SIModel(H)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', b)
infected_nodes1 = [46]
cfg.add_model_initial_configuration("Infected", infected_nodes1)
model.set_initial_status(cfg)
# Simulation execution
iterations = model.iteration_bunch(it)
trends1 = model.build_trends(iterations)
node_size=10,
alpha=0.6,
width=0.2,
node_color=color)
plt.show()
#%%
import networkx as nx
import ndlib.models.ModelConfig as mc
import ndlib.models.epidemics.SIModel as si
# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)
# Model selection
model = si.SIModel(g)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.01)
cfg.add_model_parameter("percentage_infected", 0.05)
model.set_initial_status(cfg)
# Simulation execution
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
from bokeh.io import output_notebook, show
from ndlib.viz.bokeh.DiffusionTrend import DiffusionTrend
viz1 = DiffusionTrend(model, trends)
p = viz1.plot(width=400, height=400)
# show(p)
# print the adjacency list to terminal
try:
nx.write_adjlist(G, sys.stdout)
except TypeError: # Python 3.x
nx.write_adjlist(G, sys.stdout.buffer)
nx.draw(G,node_size=50, with_labels=True)
plt.show()
# Model Selection
#model = sir.SIRModel(h)
model = si.SIModel(h)
#print(model)
# Each model has its own parameters: in order to completely
# instantiate the simulation we need to specify them using a
# Configuration object:
# Model Configuration
config = mc.Configuration()
#infection probability
config.add_model_parameter('beta',0.05)
#config.add_model_parameter('beta', 0.04)
#recovery probability
#config.add_model_parameter('lambda', 0.01)
#removal probability